Remotely sensing potential climate change tipping points across scales

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Jan. 6, 2024

Potential climate tipping points pose a growing risk for societies, and policy is calling improved anticipation of them. Satellite remote sensing can play unique role in identifying anticipating phenomena across scales. Where satellite records are too short temporal early warning points, complementary spatial indicators leverage the exceptional spatial-temporal coverage remotely sensed data to detect changing resilience vulnerable systems. Combining Earth observation with system models improve process-based understanding their interactions, potential cascades. Such fine-resolution support point management

Language: Английский

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Challenges and Limitations of Remote Sensing Applications in Northern Peatlands: Present and Future Prospects

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(3), P. 591 - 591

Published: Feb. 4, 2024

This systematic literature review (SLR) provides a comprehensive overview of remote sensing (RS) applications in northern peatlands from 2017 to 2022, utilising various platforms, including situ, UAV, airborne, and satellite technologies. It addresses the challenges limitations presented by sophisticated nature peatland ecosystems. SLR reveals an in-creased focus on mapping, monitoring, hydrology but identifies noticeable gaps degradation research. Despite benefits sensing, such as extensive spatial coverage consistent persist, high costs, underexplored areas, hyperspectral data application. Fusing with on-site research offers new insights for regional studies. However, arise issues like cost high-resolution data, limitations, inadequate field validation areas. suggests refining methodologies, validating addressing these future

Language: Английский

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Monitoring peatland water table depth with optical and radar satellite imagery

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2022, Volume and Issue: 112, P. 102866 - 102866

Published: June 17, 2022

Peatland water table depth (WTD) and wetness have widely been monitored with optical synthetic aperture radar (SAR) remote sensing but there is a lack of studies that used multi-sensor data, i.e., combination SAR data. We assessed how well WTD can be whether approach boosts explanatory capacity are differences in regression performance between data peatland types. Our consisted continuous multiannual from altogether 50 restored undrained Finnish peatlands, (Landsat 5–8, Sentinel-2) Sentinel-1 C-band processed Google Earth Engine. calculated random forest regressions dependent variable being independent variables consisting 21 10 metrics. The average was moderate models (R2 43.1%, nRMSE 19.8%), almost as high 42.4%, 19.9%) considerably lower 21.8%, 23.4%) trained separately for each site. When the included several sites were six habitat type management option combinations, R2 40.6% models, 36.6% 33.7% models. There considerable site-specific variation model −3.3–88.8% ran site) multi-sensor, or performed best. higher than open sparsely treed densely peatlands. most important SWIR-based metrics VV backscatter. results suggest works usually better does monitoring increases moderately little.

Language: Английский

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Hidden becomes clear: Optical remote sensing of vegetation reveals water table dynamics in northern peatlands

Remote Sensing of Environment, Journal Year: 2023, Volume and Issue: 296, P. 113736 - 113736

Published: July 27, 2023

The water table and its dynamics are one of the key variables that control peatland greenhouse gas exchange. Here, we tested applicability Optical TRApezoid Model (OPTRAM) to monitor temporal fluctuations in over intact, restored (previously forestry-drained), drained (under agriculture) northern peatlands Finland, Estonia, Sweden, Canada, USA. More specifically, studied potential limitations OPTRAM using data from 2018 through 2021, across 53 sites, i.e., covering largest geographical extent used studies so far. For this, calculated based on Sentinel-2 with Google Earth Engine cloud platform. First, found choice vegetation index utilised does not significantly affect performance peatlands. Second, revealed tree cover density is a major factor controlling sensitivity Tree greater than 50% led clear decrease performance. Finally, demonstrated relationship between often disappears when WT deepens (ranging 0 −100 cm, depending site location). We identified where ceases be sensitive variations highly site-specific. Overall, our results support application intact low (below 50%) varies shallow moderately deep. Our study makes significant steps towards broader implementation optical remote sensing for monitoring subsurface moisture conditions region.

Language: Английский

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Multi-sensor satellite imagery reveals spatiotemporal changes in peatland water table after restoration

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 306, P. 114144 - 114144

Published: March 30, 2024

Language: Английский

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A hybrid method for evaluating the resilience of urban road traffic network under flood disaster: An example of Nanjing, China

Environmental Science and Pollution Research, Journal Year: 2022, Volume and Issue: 29(30), P. 46306 - 46324

Published: Feb. 15, 2022

Language: Английский

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Potential for Peatland Water Table Depth Monitoring Using Sentinel-1 SAR Backscatter: Case Study of Forsinard Flows, Scotland, UK

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(7), P. 1900 - 1900

Published: March 31, 2023

Peatland restoration has become a common land-use management practice in recent years, with the water table depth (WTD) being one of key monitoring elements, where it is used as proxy for various ecosystem functions. Regular, uninterrupted, and spatially representative WTD data situ can be difficult to collect, therefore, remotely sensed offer an attractive alternative landscape-scale monitoring. In this study, we illustrate application Sentinel-1 SAR backscatter near-natural restored blanket bogs Flow Country northern Scotland. Among study sites, peatlands presented smallest fluctuations (with depths typically between 0 15 cm) had most stable radar signal throughout year (~3 4 dB amplitude). Previously drained afforested undergoing were found have higher (depths up 35 cm), which also reflected shifts (up ~6 difference within year). Sites more advanced methods been applied, however, associated shallower smoother surfaces. Three models—simple linear regression, multiple random forest model—were evaluated their potential predict dynamics using backscatter. The model was suited, highest correlation scores, lowest RMSE values, overall good temporal fit (R2 = 0.66, 2.1 regression came close second 0.59, 4.5 cm). impact standing water, terrain ruggedness, ridge furrow aspect on scores tested but not statistically significant influence. We propose that approach, models WTD, strong should wider range peatland sites.

Language: Английский

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An Overview of Remote Sensing Data Applications in Peatland Research Based on Works from the Period 2010–2021

Land, Journal Year: 2021, Volume and Issue: 11(1), P. 24 - 24

Published: Dec. 24, 2021

In the 21st century, remote sensing (RS) has become increasingly employed in many environmental studies. This paper constitutes an overview of works utilising RS methods studies on peatlands and investigates publications from period 2010–2021. Based fifty-nine case different climatic zones (from subarctic to subtropical), we can indicate increase use peatland research during last decade, which is likely a result greater availability new data sets (Sentinel 1 2; Landsat 8; SPOT 6 7) paired with rapid development open-source software (ESA SNAP; QGIS SAGA GIS). studied works, satellite analyses typically encompassed following elements: land classification/identification peatlands, changes water conditions monitoring state, vegetation mapping, Gross Primary Productivity (GPP), estimation carbon resources peatlands. The most frequently methods, other hand, included: indices, soil moisture supervised classification machine learning. Remote combined field deemed helpful for multi-proxy studies, they may offer perspectives at regional level.

Language: Английский

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Assessing the Potential of using Sentinel-1 and 2 or high-resolution aerial imagery data with Machine Learning and Data Science Techniques to Model Peatland Restoration Progress – a Northern Scotland case study

International Journal of Remote Sensing, Journal Year: 2023, Volume and Issue: 44(9), P. 2885 - 2911

Published: May 3, 2023

Peatland is a globally important store of carbon. restoration efforts are being increasingly undertaken yet effective monitoring landscape-scale projects has been limited. A particular gap in our understanding the length time required before site reaches target state. To address this, classification model based on remote sensing data was developed for peatland area blanket bog northern Scotland, UK, to evaluate whether post-restoration trajectories followed predictable trends over time. The trained against chronosequence sites within 20 × 10 km study that restored following drainage and intensive non-native afforestation. Two versions were created compare accuracy obtainable from suite Sentinel-2 satellite versus sub-metre resolution aerial imagery GetMapping (RGB IR). greatly outperformed imagery-based model. Adding surface slope did not significantly improve prediction. Prediction starting land covers very robust, both most recent oldest well predicted spatially. main uncertainties intermediate age, which underwent additional treatments after initial restoration. Using standard vegetation wetness indices as indicators, it possible track progression areas had felled rewetted towards spectral signal control locations. further examined use multiple years (2015–2021) including Sentinel-1 SAR imagery, confirmed findings obtained with only single climatically average year, furthermore efficacy different methods. We observed consistent beginning resemble hydrologically ecologically functional state 10–20 post intervention.

Language: Английский

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Managing climate-change refugia to prevent extinctions

Trends in Ecology & Evolution, Journal Year: 2024, Volume and Issue: 39(9), P. 800 - 808

Published: Sept. 1, 2024

HighlightsClimate-change refugia can support biodiversity by maintaining buffered conditions despite climate change and are a critical tool for the unfolding extinction crisis.Despite their capacity to protect biodiversity, climate-change will be increasingly vulnerable impacts of multiple interacting stressors may hence require management.Effective protection under facilitated managing or newly establishing on basis factors processes that create them.Using four clear steps, appropriate actions maintain refugia, ranging from minimal management more extensive restoration efforts, determined.Identifying reduce extinctions contribute landscapes holistically managed conservation change.AbstractEarth is facing simultaneous crises. Climate-change – areas relatively help address both these problems components when surrounding landscape no longer can. However, this often severe other stressors. Thus, need consider complex multidimensional nature refugia. We outline an approach understand refugia-promoting evaluate refugial determine suitable actions. Our framework applies as tools facilitate resistance in modern planning. Such refugia-focused change.

Language: Английский

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